MTech in Electronics and Electrical Engineering

(Specialization: Signal Processing)

           

 

Semester I

 

 

 

Semester II

 

Course No

Course Name

L-T-P-C

 

Course No

Course Name

L-T-P-C

EC 520

Linear Algebra and Random Processes

4-0-0-8

 

EC 522

Statistical Signal Processing

3-0-0-6

EC 521

Signal Processing

3-0-0-6

 

EC 523

Digital Signal Processors

2-0-3-7

EC 6xx

Dept. Elective – I

3-0-0/2-6/8

 

EC 6xx

Dept. Elective – III

3-0-0/2-6/8

EC 6xx

Dept. Elective – II

3-0-0/2-6/8

 

EC 6xx

Dept. Elective – IV

3-0-0/2-6/8

 

Total

13-0-0/4-26/30

 

EC 697

Project Phase – I

0-0-6-6

 

 

 

 

 

Total

11-0-9/13-31/35

 

Semester III

 

 

 

Semester IV

 

EC 698

Project Phase – II

0-0-24-24

 

EC 699

Project Phase - III

0-0-24-24

 

Total

0-0-24-24

 

 

Total

0-0-24-24

 

                                   

EC 520     LINEAR ALGEBRA AND RANDOM PROCESSES       (4 0 0 8)                                   

 

 

Linear Algebra: Basic analysis and topology. Vector spaces, linear operators and matrices. Decomposition theorems and eigen-analysis. Quadratic forms. Perron-Frobenius theorems. Probability: Spaces and random variables. Distributions. Transformations and moment analysis. Stochastic processes and covariance analysis. Estimation theory.

 

 

Texts/References:

 

1.        K. Hoffman and R. Kunze, Introduction to Linear Algebra, 2nd Ed, Prentice-Hall, 1996.

2.        R. Horn and C. Johnson, Matrix Analysis; Cambridge, CUP, 1991

3.        A. Papoulis, Probability, Random Variables and Stochastic Processes, 3rd Ed, McGraw-

         Hill, 1991.

4.        H. Stark and J. W. Woods, Probability, Random Variables and Estimation Theory for Engineers,  Prentice Hall, 1994.

 

 

EC 521                         Signal Processing                                (3-0-0-6)

 

Continuous-time and discrete-time signals and systems; Spectral analysis: CTFT and DTFT, DFT, FFT and STFT; Sampling, Quantization, Decimation and Interpolation; Z-transform: definition and ROC; Digital filters: FIR and IIR filters, Digital-filter realisations and design, Finite wordlength effects; Adaptive filtering: steepest-descent algorithm, LMS, variants of LMS, LS, RLS, blind algorithms.

 

Texts/References:

 

1.  S. Haykin, Adaptive Filter Theory, PHI, 2001.

2. A.V. Oppenheim and R.W. Schafer, Discrete- Time Signal Processing, PHI, 2000.
3. S. K. Mitra, Digital Signal Processing, TMH, 3/e, 2006.
4. S. J. Orfanidis, Introduction to Digital Signal Processing, Prentice-Hall, 1996

 

 

 

EC 522                         Statistical Signal Processing   (3-0-0-6)      

 

Review of signals, systems and linear algebra; Review of random variables; Review of random processes: LSI system with random input signal, Paley-Wiener criterion, spectral factorization theorem, Wold’s decomposition; Random signal modeling: MA, AR, ARMA models; Parameter estimation: necessary and sufficient statistic, CRLB, maximum likelihood and Bayesian estimation; Optimal linear filtering: LMMSE, WH equations, FIR and IIR Wiener filters; Linear Prediction: Yule-Walker equations, Levinson-Durbin Algorithm, lattice filter; Adaptive filtering from Wiener filtering prospective; Kalman filters; Spectral estimation: periodograms, modified periodograms, minimum variance, maximum entropy and parametric methods for spectral estimation.


Texts/References:


1. M. H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley & Sons, Inc., 2002.

2. S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Prentice Hall,

   1993.

3. J. G. Proakis et. al., Algorithms for Statistical Signal Processing, Pearson Education, 2002.

4. H. Stark and J. W. Woods, Probability and Random Processes with Application to Signal Processing, PHI, 2002.

5. S. Haykin, Adaptive Filter Theory, PHI, 2001.

 

 

EC 523             Digital Signal Processors                     (2-0-3-7)                        [New]

 

Introduction: Computational characteristics of DSP algorithms and applications; Techniques for enhancing computational throughput: Harvard architecture, parallelism, pipelining, dedicated multiplier, split ALU and barrel shifter; TMS320C64xx architecture: CPU data paths and control, general purpose register files,  register file cross paths, memory load and store paths, data address paths, parallel operations, resource constraints; Assembly language: Programmers model, functional units, Fetch and execute packets, pipelining, linear and circular addressing, assembler directives, addressing modes, instructions; Memory: Program memory, data memory, memory configuration. External memory interface (EMIF), fixed point and floating point formats; Interrupts: Interrupt sources, interrupt control registers and interrupt acknowledgment; Peripherals: Timer, multi channel buffered serial port, DMA, general purpose IO; DSP Real Time system operating systems; Applications: a few case studies of application of DSPs in communication and multimedia.

 

Laboratory

 

Experiments: Familiarization to Code Composer Studio; development cycle on TMS320C64xx kit;  finite impluse response filter;  infinite impulse response filter; adaptive filter and experiments on communication such as generation of a n-tuple PN sequence, generation of a white noise sequence using the PN sequence and CLT, restoration of a sinusiodal signal embedded in white noise by Wiener

 

Filtering;  speech and multi-media applications.

 

Texts/References:

 

1.Rulph Chassaing and Donald Reay, Digital signal processing and applications with Tms320C6713 and TMS320C6416, Wiley, 2008.

2.TMS320C64x Technical Overview, Texas Instruments, Dallas, TX, 2001.

3.TMS320C6000 Peripherals Reference Guide,  Texas Instruments, Dallas, TX,  2001.

4.TMS320C6000 CPU and Instruction Set Reference Guide,  Texas Instruments, Dallas, TX, 2000.

5.IEEE Signal Processing Magazine : Oct 88, Jan 89, July 97, Jan 98, March 98 and March 2000.